{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "429489c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4f72c3b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import collections\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data.dataloader import DataLoader\n",
    "from tqdm import tqdm\n",
    "import h5py\n",
    "import pandas as pd\n",
    "import json\n",
    "import tabulate\n",
    "\n",
    "from numpy import dot\n",
    "from numpy.linalg import norm\n",
    "\n",
    "def cos_sim(a,b):\n",
    "    return dot(a, b)/(norm(a)*norm(b))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "db44c0b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lxrt.tokenization:loading vocabulary file C:/Users/lab/work/Anna/nlp-tutorial/5-2.BERT/bert-base-multilingual-uncased\\vocab.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.symbolic_embedding_number: 4003\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from param import args\n",
    "\n",
    "'''predefined at param.py'''\n",
    "args.config = r'configs/vqa_multilingual.json'\n",
    "args.output = r'snap/vqa_multilingual_model'\n",
    "args.test = 'val'\n",
    "#args.load = r'snap/vqa'\n",
    "args.weight_disable = True\n",
    "args.kl_divergence = False\n",
    "args.multiGPU =False\n",
    "args.num_workers =0\n",
    "args.save_folder =\"test\"\n",
    "\n",
    "'''modify src\\tools\\sharearray cache path to current folder'''\n",
    "\n",
    "from pretrain.qa_answer_table import AnswerTable,load_lxmert_qa, load_lxmert_from_sgg_and_lxmert_pretrain, load_lxmert_from_pretrain_noqa\n",
    "from tasks.vqa_model import VQAModel\n",
    "from tasks.vqa_data import VQADataset, VQATorchDataset, VQAEvaluator\n",
    "\n",
    "from utils import load_lxmert_sgg\n",
    "\n",
    "'''load tokenizer'''\n",
    "from lxrt.tokenization import BertTokenizer\n",
    "\n",
    "tokenizer = BertTokenizer.from_pretrained(\n",
    "    args.huggingface+args.tokenizer,\n",
    "    do_lower_case=True\n",
    ")\n",
    "\n",
    "DataTuple = collections.namedtuple(\"DataTuple\", 'dataset loader evaluator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9e1ba957",
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.PWI_func import VQA,PWI_FixedReplace_QuesModify_Embed,a2b,b2c,shift,list_mul,get_data_tuple"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "658d754b",
   "metadata": {},
   "source": [
    "# '''load trained VQA model'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9d03d5cb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "None\n",
      "snap/vqa_multilingual/Epoch45GEOnly.pth\n",
      "splits: train,nominival\n",
      "Load 632117 data from split(s) train,nominival.\n",
      "['train']\n",
      "'train2014_obj36.h5_boxes': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_id': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_conf': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_id': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_conf': returning memmapped view.\n",
      "data/mscoco_imgfeat/train2014_obj36.h5 82783\n",
      "Created ['train']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lxrt.tokenization:loading vocabulary file C:/Users/lab/work/Anna/nlp-tutorial/5-2.BERT/bert-base-multilingual-uncased\\vocab.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use 443757 data in torch dataset\n",
      "\n",
      "data/vocabs/attributes_vocab.txt\n",
      "data/vocabs/attributes_de_vocab.txt\n",
      "data/vocabs/objects_vocab.txt\n",
      "data/vocabs/objects_de_vocab.txt\n",
      "splits: minival\n",
      "Load 25994 data from split(s) minival.\n",
      "['minival']\n",
      "'val2014_obj36.h5_boxes': returning memmapped view.\n",
      "'val2014_obj36.h5_objects_id': returning memmapped view.\n",
      "'val2014_obj36.h5_objects_conf': returning memmapped view.\n",
      "'val2014_obj36.h5_attrs_id': returning memmapped view.\n",
      "'val2014_obj36.h5_attrs_conf': returning memmapped view.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lxrt.tokenization:loading vocabulary file C:/Users/lab/work/Anna/nlp-tutorial/5-2.BERT/bert-base-multilingual-uncased\\vocab.txt\n",
      "INFO:lxrt.tokenization:loading vocabulary file C:/Users/lab/work/Anna/nlp-tutorial/5-2.BERT/bert-base-multilingual-uncased\\vocab.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/mscoco_imgfeat/val2014_obj36.h5 40504\n",
      "Created ['minival']\n",
      "Use 25994 data in torch dataset\n",
      "\n",
      "data/vocabs/attributes_vocab.txt\n",
      "data/vocabs/attributes_de_vocab.txt\n",
      "data/vocabs/objects_vocab.txt\n",
      "data/vocabs/objects_de_vocab.txt\n",
      "#################################\n",
      "args.output_attention: True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lxrt.modeling:loading archive file C:/Users/lab/work/Anna/nlp-tutorial/5-2.BERT/bert-base-multilingual-uncased\n",
      "INFO:lxrt.modeling:Model config {\n",
      "  \"architectures\": [\n",
      "    \"BertForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"directionality\": \"bidi\",\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"layer_norm_eps\": 1e-12,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"bert\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"pooler_fc_size\": 768,\n",
      "  \"pooler_num_attention_heads\": 12,\n",
      "  \"pooler_num_fc_layers\": 3,\n",
      "  \"pooler_size_per_head\": 128,\n",
      "  \"pooler_type\": \"first_token_transform\",\n",
      "  \"symbolic_embedding_number\": 4003,\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"vocab_size\": 105879\n",
      "}\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LXRT encoder with 12 l_layers, 0 x_layers, and 0 r_layers.\n",
      "\n",
      "\n",
      "!! Has 12 layers\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lxrt.modeling:Weights of LXRTFeatureExtractionAttention not initialized from pretrained model: ['bert.embeddings.symbolic_embedding.weight', 'bert.embeddings.token_type_embeddings_visual.weight', 'bert.embeddings.position_embeddings_visual.weight', 'bert.embeddings.visn_fc.weight', 'bert.embeddings.visn_fc.bias', 'bert.embeddings.visn_layer_norm.weight', 'bert.embeddings.visn_layer_norm.bias', 'bert.embeddings.box_fc.weight', 'bert.embeddings.box_fc.bias', 'bert.embeddings.box_layer_norm.weight', 'bert.embeddings.box_layer_norm.bias', 'bert.embeddings.tag_layer_norm.weight', 'bert.embeddings.tag_layer_norm.bias', 'bert.encoder.visn_fc.visn_fc.weight', 'bert.encoder.visn_fc.visn_fc.bias', 'bert.encoder.visn_fc.visn_layer_norm.weight', 'bert.encoder.visn_fc.visn_layer_norm.bias', 'bert.encoder.visn_fc.box_fc.weight', 'bert.encoder.visn_fc.box_fc.bias', 'bert.encoder.visn_fc.box_layer_norm.weight', 'bert.encoder.visn_fc.box_layer_norm.bias']\n",
      "INFO:lxrt.modeling:Weights from pretrained model not used in LXRTFeatureExtractionAttention: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
      "INFO:lxrt.tokenization:loading vocabulary file C:/Users/lab/work/Anna/nlp-tutorial/5-2.BERT/bert-base-multilingual-uncased\\vocab.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load QA pre-trained LXMERT from snap/vqa_multilingual/Epoch45GEOnly.pth \n",
      "Loaded 0 answers from LXRTQA pre-training and 0 not\n",
      "\n",
      "BertAdam Total Iters: 693350\n",
      "BERTAdam lr 5e-05 total_steps 693350  warmup 0.1\n"
     ]
    }
   ],
   "source": [
    "\n",
    "'''load vqa fine-tuned model'''\n",
    "args.load_lxmert_pretrain = None# \"snap/pretrained/CC_Unsupervised_LXRT.pth\"\n",
    "args.load_lxmert_qa = \"snap/vqa_multilingual/BEST.pth\"\n",
    "\n",
    "epoch=45\n",
    "\n",
    "args.load_lxmert_qa = \"snap/vqa_multilingual/Epoch%dGEOnly.pth\"%epoch\n",
    "\n",
    "\n",
    "\n",
    "args.load_lxmert = None\n",
    "print(args.load_lxmert)\n",
    "print(args.load_lxmert_pretrain)\n",
    "print(args.load_lxmert_qa)\n",
    "\n",
    "vqa = VQA();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "4873d30d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 ['wagen', 'zug', 'flugzeug', 'fernsehen', 'uhr', 'telefon', 'kamera', 'hund', 'kuh', 'baum', 'berg']\n",
      "11 ['hund', 'kuh', 'ente', 'mais', 'stein', 'blatt', 'zeitschrift', 'wagen', 'zug', 'wein', 'haar']\n",
      "11 11 11\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "object_file = \"object_list_bilingual_train\"\n",
    "object_file = \"object_list_bilingual_train_GETags\"\n",
    "\n",
    "if object_file == \"object_list_bilingual_train\":\n",
    "    from data.PWI.object_list_bilingual import food_list,animal_list,food_list_cate,pseudo_list,hypernymy_list\n",
    "elif object_file == \"object_list_bilingual_train_GETags\":\n",
    "    from data.PWI.object_list_bilingual_GETags import food_list,animal_list,food_list_cate,pseudo_list,hypernymy_list\n",
    "    \n",
    "print(len(food_list),food_list)\n",
    "print(len(animal_list),animal_list)\n",
    "print(len(food_list_cate),len(pseudo_list),len(hypernymy_list))\n",
    "Nscan = len(food_list)\n",
    "print(Nscan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1ae1cdd4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 ['wagen', 'zug', 'flugzeug', 'fernsehen', 'uhr', 'telefon', 'kamera', 'hund', 'kuh', 'baum', 'berg']\n",
      "11 ['hund', 'kuh', 'ente', 'mais', 'stein', 'blatt', 'zeitschrift', 'wagen', 'zug', 'wein', 'haar']\n",
      "875\n",
      "train_Anna_embed_v2\n",
      "train_Anna_embed_v2\n",
      "splits: train_Anna_embed_v2\n",
      "Load 875 data from split(s) train_Anna_embed_v2.\n",
      "['train']\n",
      "'train2014_obj36.h5_boxes': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_id': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_conf': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_id': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_conf': returning memmapped view.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lxrt.tokenization:loading vocabulary file C:/Users/lab/work/Anna/nlp-tutorial/5-2.BERT/bert-base-multilingual-uncased\\vocab.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/mscoco_imgfeat/train2014_obj36.h5 82783\n",
      "Created ['train']\n",
      "Use 875 data in torch dataset\n",
      "\n",
      "data/vocabs/attributes_vocab.txt\n",
      "data/vocabs/attributes_de_vocab.txt\n",
      "data/vocabs/objects_vocab.txt\n",
      "data/vocabs/objects_de_vocab.txt\n",
      "[1652]\n",
      "[1768]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 14/14 [00:07<00:00,  1.88it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "875\n",
      "483\n",
      "483\n",
      "483\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 14/14 [00:07<00:00,  1.89it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "483\n",
      "11 ['wagen', 'zug', 'flugzeug', 'fernsehen', 'uhr', 'telefon', 'kamera', 'hund', 'kuh', 'baum', 'berg']\n",
      "11 ['hund', 'kuh', 'ente', 'mais', 'stein', 'blatt', 'zeitschrift', 'wagen', 'zug', 'wein', 'haar']\n",
      "873\n",
      "train_Anna_embed_v2\n",
      "train_Anna_embed_v2\n",
      "splits: train_Anna_embed_v2\n",
      "Load 873 data from split(s) train_Anna_embed_v2.\n",
      "['train']\n",
      "'train2014_obj36.h5_boxes': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_id': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_conf': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_id': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_conf': returning memmapped view.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lxrt.tokenization:loading vocabulary file C:/Users/lab/work/Anna/nlp-tutorial/5-2.BERT/bert-base-multilingual-uncased\\vocab.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/mscoco_imgfeat/train2014_obj36.h5 82783\n",
      "Created ['train']\n",
      "Use 873 data in torch dataset\n",
      "\n",
      "data/vocabs/attributes_vocab.txt\n",
      "data/vocabs/attributes_de_vocab.txt\n",
      "data/vocabs/objects_vocab.txt\n",
      "data/vocabs/objects_de_vocab.txt\n",
      "[1670]\n",
      "[2100]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 14/14 [00:06<00:00,  2.07it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "873\n",
      "447\n",
      "447\n",
      "447\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 14/14 [00:06<00:00,  2.08it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "447\n",
      "11 ['wagen', 'zug', 'flugzeug', 'fernsehen', 'uhr', 'telefon', 'kamera', 'hund', 'kuh', 'baum', 'berg']\n",
      "11 ['hund', 'kuh', 'ente', 'mais', 'stein', 'blatt', 'zeitschrift', 'wagen', 'zug', 'wein', 'haar']\n",
      "763\n",
      "train_Anna_embed_v2\n",
      "train_Anna_embed_v2\n",
      "splits: train_Anna_embed_v2\n",
      "Load 763 data from split(s) train_Anna_embed_v2.\n",
      "['train']\n",
      "'train2014_obj36.h5_boxes': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_id': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_conf': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_id': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_conf': returning memmapped view.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lxrt.tokenization:loading vocabulary file C:/Users/lab/work/Anna/nlp-tutorial/5-2.BERT/bert-base-multilingual-uncased\\vocab.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/mscoco_imgfeat/train2014_obj36.h5 82783\n",
      "Created ['train']\n",
      "Use 763 data in torch dataset\n",
      "\n",
      "data/vocabs/attributes_vocab.txt\n",
      "data/vocabs/attributes_de_vocab.txt\n",
      "data/vocabs/objects_vocab.txt\n",
      "data/vocabs/objects_de_vocab.txt\n",
      "[1731]\n",
      "[1448]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:06<00:00,  1.73it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "763\n",
      "2\n",
      "2\n",
      "2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "11 ['wagen', 'zug', 'flugzeug', 'fernsehen', 'uhr', 'telefon', 'kamera', 'hund', 'kuh', 'baum', 'berg']\n",
      "11 ['hund', 'kuh', 'ente', 'mais', 'stein', 'blatt', 'zeitschrift', 'wagen', 'zug', 'wein', 'haar']\n",
      "352\n",
      "train_Anna_embed_v2\n",
      "train_Anna_embed_v2\n",
      "splits: train_Anna_embed_v2\n",
      "Load 352 data from split(s) train_Anna_embed_v2.\n",
      "['train']\n",
      "'train2014_obj36.h5_boxes': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_id': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_conf': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_id': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_conf': returning memmapped view.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lxrt.tokenization:loading vocabulary file C:/Users/lab/work/Anna/nlp-tutorial/5-2.BERT/bert-base-multilingual-uncased\\vocab.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/mscoco_imgfeat/train2014_obj36.h5 82783\n",
      "Created ['train']\n",
      "Use 352 data in torch dataset\n",
      "\n",
      "data/vocabs/attributes_vocab.txt\n",
      "data/vocabs/attributes_de_vocab.txt\n",
      "data/vocabs/objects_vocab.txt\n",
      "data/vocabs/objects_de_vocab.txt\n",
      "[1643]\n",
      "[1787]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "4\n",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "4\n",
      "11 ['wagen', 'zug', 'flugzeug', 'fernsehen', 'uhr', 'telefon', 'kamera', 'hund', 'kuh', 'baum', 'berg']\n",
      "11 ['hund', 'kuh', 'ente', 'mais', 'stein', 'blatt', 'zeitschrift', 'wagen', 'zug', 'wein', 'haar']\n",
      "438\n",
      "train_Anna_embed_v2\n",
      "train_Anna_embed_v2\n",
      "splits: train_Anna_embed_v2\n",
      "Load 438 data from split(s) train_Anna_embed_v2.\n",
      "['train']\n",
      "'train2014_obj36.h5_boxes': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_id': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_conf': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_id': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_conf': returning memmapped view.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lxrt.tokenization:loading vocabulary file C:/Users/lab/work/Anna/nlp-tutorial/5-2.BERT/bert-base-multilingual-uncased\\vocab.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/mscoco_imgfeat/train2014_obj36.h5 82783\n",
      "Created ['train']\n",
      "Use 438 data in torch dataset\n",
      "\n",
      "data/vocabs/attributes_vocab.txt\n",
      "data/vocabs/attributes_de_vocab.txt\n",
      "data/vocabs/objects_vocab.txt\n",
      "data/vocabs/objects_de_vocab.txt\n",
      "[1718]\n",
      "[94]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:03<00:00,  1.97it/s]\n",
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     ]
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "236\n",
      "11 ['wagen', 'zug', 'flugzeug', 'fernsehen', 'uhr', 'telefon', 'kamera', 'hund', 'kuh', 'baum', 'berg']\n",
      "11 ['hund', 'kuh', 'ente', 'mais', 'stein', 'blatt', 'zeitschrift', 'wagen', 'zug', 'wein', 'haar']\n",
      "1427\n",
      "train_Anna_embed_v2\n",
      "train_Anna_embed_v2\n",
      "splits: train_Anna_embed_v2\n",
      "Load 1427 data from split(s) train_Anna_embed_v2.\n",
      "['train']\n",
      "'train2014_obj36.h5_boxes': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_id': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_conf': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_id': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_conf': returning memmapped view.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lxrt.tokenization:loading vocabulary file C:/Users/lab/work/Anna/nlp-tutorial/5-2.BERT/bert-base-multilingual-uncased\\vocab.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/mscoco_imgfeat/train2014_obj36.h5 82783\n",
      "Created ['train']\n",
      "Use 1427 data in torch dataset\n",
      "\n",
      "data/vocabs/attributes_vocab.txt\n",
      "data/vocabs/attributes_de_vocab.txt\n",
      "data/vocabs/objects_vocab.txt\n",
      "data/vocabs/objects_de_vocab.txt\n",
      "[2062]\n",
      "[707]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "1427\n",
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     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "429\n",
      "11 ['wagen', 'zug', 'flugzeug', 'fernsehen', 'uhr', 'telefon', 'kamera', 'hund', 'kuh', 'baum', 'berg']\n",
      "11 ['hund', 'kuh', 'ente', 'mais', 'stein', 'blatt', 'zeitschrift', 'wagen', 'zug', 'wein', 'haar']\n",
      "902\n",
      "train_Anna_embed_v2\n",
      "train_Anna_embed_v2\n",
      "splits: train_Anna_embed_v2\n",
      "Load 902 data from split(s) train_Anna_embed_v2.\n",
      "['train']\n",
      "'train2014_obj36.h5_boxes': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_id': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_conf': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_id': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_conf': returning memmapped view.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lxrt.tokenization:loading vocabulary file C:/Users/lab/work/Anna/nlp-tutorial/5-2.BERT/bert-base-multilingual-uncased\\vocab.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/mscoco_imgfeat/train2014_obj36.h5 82783\n",
      "Created ['train']\n",
      "Use 902 data in torch dataset\n",
      "\n",
      "data/vocabs/attributes_vocab.txt\n",
      "data/vocabs/attributes_de_vocab.txt\n",
      "data/vocabs/objects_vocab.txt\n",
      "data/vocabs/objects_de_vocab.txt\n",
      "[2339]\n",
      "[349]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
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    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "69\n",
      "11 ['wagen', 'zug', 'flugzeug', 'fernsehen', 'uhr', 'telefon', 'kamera', 'hund', 'kuh', 'baum', 'berg']\n",
      "11 ['hund', 'kuh', 'ente', 'mais', 'stein', 'blatt', 'zeitschrift', 'wagen', 'zug', 'wein', 'haar']\n",
      "1514\n",
      "train_Anna_embed_v2\n",
      "train_Anna_embed_v2\n",
      "splits: train_Anna_embed_v2\n",
      "Load 1514 data from split(s) train_Anna_embed_v2.\n",
      "['train']\n",
      "'train2014_obj36.h5_boxes': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_id': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_conf': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_id': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_conf': returning memmapped view.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lxrt.tokenization:loading vocabulary file C:/Users/lab/work/Anna/nlp-tutorial/5-2.BERT/bert-base-multilingual-uncased\\vocab.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/mscoco_imgfeat/train2014_obj36.h5 82783\n",
      "Created ['train']\n",
      "Use 1514 data in torch dataset\n",
      "\n",
      "data/vocabs/attributes_vocab.txt\n",
      "data/vocabs/attributes_de_vocab.txt\n",
      "data/vocabs/objects_vocab.txt\n",
      "data/vocabs/objects_de_vocab.txt\n",
      "[1717]\n",
      "[1930]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "1514\n",
      "778\n",
      "778\n",
      "778\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "778\n",
      "11 ['wagen', 'zug', 'flugzeug', 'fernsehen', 'uhr', 'telefon', 'kamera', 'hund', 'kuh', 'baum', 'berg']\n",
      "11 ['hund', 'kuh', 'ente', 'mais', 'stein', 'blatt', 'zeitschrift', 'wagen', 'zug', 'wein', 'haar']\n",
      "874\n",
      "train_Anna_embed_v2\n",
      "train_Anna_embed_v2\n",
      "splits: train_Anna_embed_v2\n",
      "Load 874 data from split(s) train_Anna_embed_v2.\n",
      "['train']\n",
      "'train2014_obj36.h5_boxes': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_id': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_conf': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_id': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_conf': returning memmapped view.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lxrt.tokenization:loading vocabulary file C:/Users/lab/work/Anna/nlp-tutorial/5-2.BERT/bert-base-multilingual-uncased\\vocab.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/mscoco_imgfeat/train2014_obj36.h5 82783\n",
      "Created ['train']\n",
      "Use 874 data in torch dataset\n",
      "\n",
      "data/vocabs/attributes_vocab.txt\n",
      "data/vocabs/attributes_de_vocab.txt\n",
      "data/vocabs/objects_vocab.txt\n",
      "data/vocabs/objects_de_vocab.txt\n",
      "[1697]\n",
      "[1001]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
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    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "337\n",
      "11 ['wagen', 'zug', 'flugzeug', 'fernsehen', 'uhr', 'telefon', 'kamera', 'hund', 'kuh', 'baum', 'berg']\n",
      "11 ['hund', 'kuh', 'ente', 'mais', 'stein', 'blatt', 'zeitschrift', 'wagen', 'zug', 'wein', 'haar']\n",
      "959\n",
      "train_Anna_embed_v2\n",
      "train_Anna_embed_v2\n",
      "splits: train_Anna_embed_v2\n",
      "Load 959 data from split(s) train_Anna_embed_v2.\n",
      "['train']\n",
      "'train2014_obj36.h5_boxes': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_id': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_conf': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_id': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_conf': returning memmapped view.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lxrt.tokenization:loading vocabulary file C:/Users/lab/work/Anna/nlp-tutorial/5-2.BERT/bert-base-multilingual-uncased\\vocab.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/mscoco_imgfeat/train2014_obj36.h5 82783\n",
      "Created ['train']\n",
      "Use 959 data in torch dataset\n",
      "\n",
      "data/vocabs/attributes_vocab.txt\n",
      "data/vocabs/attributes_de_vocab.txt\n",
      "data/vocabs/objects_vocab.txt\n",
      "data/vocabs/objects_de_vocab.txt\n",
      "[1891]\n",
      "[1539]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 15/15 [00:08<00:00,  1.80it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "959\n",
      "447\n",
      "447\n",
      "447\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 15/15 [00:09<00:00,  1.61it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "447\n",
      "11 ['wagen', 'zug', 'flugzeug', 'fernsehen', 'uhr', 'telefon', 'kamera', 'hund', 'kuh', 'baum', 'berg']\n",
      "11 ['hund', 'kuh', 'ente', 'mais', 'stein', 'blatt', 'zeitschrift', 'wagen', 'zug', 'wein', 'haar']\n",
      "529\n",
      "train_Anna_embed_v2\n",
      "train_Anna_embed_v2\n",
      "splits: train_Anna_embed_v2\n",
      "Load 529 data from split(s) train_Anna_embed_v2.\n",
      "['train']\n",
      "'train2014_obj36.h5_boxes': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_id': returning memmapped view.\n",
      "'train2014_obj36.h5_objects_conf': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_id': returning memmapped view.\n",
      "'train2014_obj36.h5_attrs_conf': returning memmapped view.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lxrt.tokenization:loading vocabulary file C:/Users/lab/work/Anna/nlp-tutorial/5-2.BERT/bert-base-multilingual-uncased\\vocab.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/mscoco_imgfeat/train2014_obj36.h5 82783\n",
      "Created ['train']\n",
      "Use 529 data in torch dataset\n",
      "\n",
      "data/vocabs/attributes_vocab.txt\n",
      "data/vocabs/attributes_de_vocab.txt\n",
      "data/vocabs/objects_vocab.txt\n",
      "data/vocabs/objects_de_vocab.txt\n",
      "[1812]\n",
      "[2862]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:04<00:00,  1.95it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "529\n",
      "95\n",
      "95\n",
      "95\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:05<00:00,  1.58it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:04<00:00,  1.94it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:04<00:00,  1.95it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:04<00:00,  1.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1008x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''use original questions'''\n",
    "def quesfunc(sent):\n",
    "    return sent\n",
    "\n",
    "from unidecode import unidecode\n",
    "'''modified for multilingual VQA'''\n",
    "symbolic_objects= [unidecode(s).lower() for s in tset.symbolic_vocab.objects]\n",
    "\n",
    "for food_idid in range(0,Nscan):\n",
    "    if object_file == \"object_list_bilingual_train\":\n",
    "        from data.PWI.object_list_bilingual import food_list,animal_list,food_list_cate,pseudo_list,hypernymy_list\n",
    "    elif object_file == \"object_list_bilingual_train_GETags\":\n",
    "        from data.PWI.object_list_bilingual_GETags import food_list,animal_list,food_list_cate,pseudo_list,hypernymy_list\n",
    "    \n",
    "    \n",
    "    print(len(food_list),food_list)\n",
    "    print(len(animal_list),animal_list)\n",
    "\n",
    "    #food_idid=1\n",
    "    food_list=[food_list[food_idid]]\n",
    "    \n",
    "    animal_list=[animal_list[food_idid]]\n",
    "    food_list_cate=[food_list_cate[food_idid]]\n",
    "    pseudo_list=[pseudo_list[food_idid]]\n",
    "    hypernymy_list=[hypernymy_list[food_idid]]\n",
    "    \n",
    "    if object_file == \"object_list_bilingual_train\":\n",
    "        use_list=food_list\n",
    "    elif object_file == \"object_list_bilingual_train_GETags\":\n",
    "        use_list=hypernymy_list \n",
    "        \n",
    "    target_object=food_list[0]\n",
    "    answer_item=use_list[0]\n",
    "    data = []\n",
    "    word_ref=target_object\n",
    "\n",
    "    #dataset_name='nominival'\n",
    "    dataset_name='train'\n",
    "\n",
    "    description='embed_v2'\n",
    "\n",
    "    dataset_tailor=\"{}_Anna_{}\".format(dataset_name,description)\n",
    "\n",
    "    data.extend(json.load(open(\"data/vqa/{}.json\".format(dataset_name))))\n",
    "    data_food=[]\n",
    "    for i in range(len(data)):\n",
    "        for item in data[i]['label'].keys():\n",
    "            if item in use_list:\n",
    "                data_food.append(data[i])\n",
    "    print(len(data_food))\n",
    "    import json\n",
    "    with open(\"data/vqa/\"+dataset_tailor+\".json\", 'w') as f:\n",
    "        json.dump(data_food, f)\n",
    "    print(dataset_tailor)\n",
    "\n",
    "    '''start vqa test'''\n",
    "\n",
    "    '''\n",
    "    1) dataset_tailor is the dataset with food_list in answers\n",
    "    2) keep shuffle=False for a consistent order between different model evaluations\n",
    "    '''\n",
    "\n",
    "    vqa.model.eval()\n",
    "    print(dataset_tailor)\n",
    "    eval_tuple = get_data_tuple(dataset_tailor,bs=64,shuffle=False, drop_last=False)\n",
    "\n",
    "    '''\n",
    "    1) tset.symbolic_vocab.objects\n",
    "    with a length of 1600\n",
    "    including all the objects from RNN\n",
    "    2) dset.ans2label\n",
    "    with a length of 3129\n",
    "    including all the answers from VQA\n",
    "    '''\n",
    "\n",
    "    food_obj_id = [symbolic_objects.index(food_tmp) for food_tmp in food_list]\n",
    "    food_ans_id = [dset.ans2label[food_tmp] for food_tmp in use_list]\n",
    "    print(food_obj_id)\n",
    "    print(food_ans_id)\n",
    "\n",
    "    # animal_obj_id = [tset.symbolic_vocab.objects.index(animal_tmp) for animal_tmp in animal_list]\n",
    "    # animal_ans_id = [dset.ans2label[animal_tmp] for animal_tmp in animal_list]\n",
    "    # print(animal_obj_id)\n",
    "    # print(animal_ans_id)\n",
    "    '''no tags'''\n",
    "    \n",
    "    target_list = food_list\n",
    "    use_category = food_obj_id\n",
    "    replace_obj_id = food_list # not used but need to define\n",
    "\n",
    "    quesid6 = PWI_FixedReplace_QuesModify_Embed(quesfunc,vqa,\n",
    "                               eval_tuple,\n",
    "                               tset,\n",
    "                               tokenizer,\n",
    "                               replace_obj_id,\n",
    "                               use_category,\n",
    "                               ans_filter = None,\n",
    "                               replace_en=False,\n",
    "                               feat_filter_en=True,\n",
    "                               No_tags=True\n",
    "                              )\n",
    "\n",
    "    \n",
    "    '''no replacement'''\n",
    "    target_list = food_list\n",
    "    use_category = food_obj_id\n",
    "    replace_obj_id = food_list # not used but need to define\n",
    "\n",
    "    quesid0 = PWI_FixedReplace_QuesModify_Embed(quesfunc,vqa,\n",
    "                               eval_tuple,\n",
    "                               tset,\n",
    "                               tokenizer,\n",
    "                               replace_obj_id,\n",
    "                               use_category,\n",
    "                               ans_filter = None,\n",
    "                               replace_en=False,\n",
    "                               feat_filter_en=True,\n",
    "                               No_tags=False\n",
    "                              )\n",
    "\n",
    "    LM_choice = quesid0['LM_ans']\n",
    "    print(len(LM_choice))\n",
    "    LM_choice_id = [symbolic_objects.index(_tmp) if _tmp in use_list else 9999 for _tmp in LM_choice ]\n",
    "\n",
    "    ans_intargetlist=[(i,sco,ans) for i,(sco,ans,multi_ans,N_obj) in enumerate(zip(quesid0['LM_ans_score'],\n",
    "                                                                           quesid0['LM_ans'],\n",
    "                                                                           quesid0['multi_ans'],\n",
    "                                                                           quesid0['N_obj_pick']\n",
    "                                                                                )) if ans in use_list and N_obj>0]\n",
    "    target_select_id =[i[0] for i in ans_intargetlist]\n",
    "    print(len(ans_intargetlist))\n",
    "    print(len(target_select_id))\n",
    "\n",
    "    score0=[]\n",
    "    for i,score,ans in ans_intargetlist:\n",
    "        score0.append(quesid0['logit'][i][dset.ans2label[ans]].item())\n",
    "\n",
    "    dict_use=quesid0\n",
    "    quesid_new={}\n",
    "    quesid_new['image_feats_bert']=[]\n",
    "    quesid_new['image_tags_bert']=[]\n",
    "    quesid_new['image_obj']=[]\n",
    "    for i,(feats_bert,tags_bert,obj_id) in enumerate(zip(dict_use['image_feats_bert'],\n",
    "                                                         dict_use['image_tags_bert'],\n",
    "                                                         dict_use['image_obj'])):\n",
    "        if i in target_select_id:\n",
    "            #print(i,feats_bert.shape)\n",
    "            #print(i,tags_bert.shape)\n",
    "            #print(i,obj_id)\n",
    "            quesid_new['image_feats_bert'].append(feats_bert)\n",
    "            quesid_new['image_tags_bert'].append(tags_bert)\n",
    "            quesid_new['image_obj'].append(obj_id)\n",
    "    print(len(quesid_new['image_feats_bert']))\n",
    "    \n",
    "\n",
    "    hidden_0,_ = plot_cs_singleobj(quesid_new);\n",
    "\n",
    "#     for offset_id in range(Nscan):\n",
    "\n",
    "#         if object_file == \"object_list_bilingual_train\":\n",
    "#             from data.PWI.object_list_bilingual import food_list,animal_list,food_list_cate,pseudo_list,hypernymy_list \n",
    "\n",
    "    #target_list = food_list\n",
    "    #use_category = food_obj_id\n",
    "    replace_obj_id = food_list_cate # not used but need to define\n",
    "    quesid1 = PWI_FixedReplace_QuesModify_Embed(quesfunc,vqa,\n",
    "                               eval_tuple,\n",
    "                               tset,\n",
    "                               tokenizer,\n",
    "                               replace_obj_id,\n",
    "                               use_category,\n",
    "                               ans_filter = None,\n",
    "                               replace_en=True,\n",
    "                               feat_filter_en=True,\n",
    "                               No_tags=False\n",
    "                              )\n",
    "    score_noreplace=[]\n",
    "    for i,score,ans in ans_intargetlist:\n",
    "        score_noreplace.append(quesid1['logit'][i][dset.ans2label[ans]].item())\n",
    "\n",
    "    dict_use=quesid1\n",
    "    quesid_new={}\n",
    "    quesid_new['image_feats_bert']=[]\n",
    "    quesid_new['image_tags_bert']=[]\n",
    "    quesid_new['image_obj']=[]\n",
    "    for i,(feats_bert,tags_bert,obj_id) in enumerate(zip(dict_use['image_feats_bert'],\n",
    "                                                         dict_use['image_tags_bert'],\n",
    "                                                         dict_use['image_obj'])):\n",
    "        if i in target_select_id:\n",
    "            #print(i,feats_bert.shape)\n",
    "            #print(i,tags_bert.shape)\n",
    "            #print(i,obj_id)\n",
    "            quesid_new['image_feats_bert'].append(feats_bert)\n",
    "            quesid_new['image_tags_bert'].append(tags_bert)\n",
    "            quesid_new['image_obj'].append(obj_id)\n",
    "    #print(len(quesid_new['image_feats_bert']))\n",
    "\n",
    "    hidden_1,_ = plot_cs_singleobj(quesid_new);\n",
    "\n",
    "\n",
    "    replace_obj_id = animal_list # not used but need to define\n",
    "    quesid2 = PWI_FixedReplace_QuesModify_Embed(quesfunc,vqa,\n",
    "                               eval_tuple,\n",
    "                               tset,\n",
    "                               tokenizer,\n",
    "                               replace_obj_id,\n",
    "                               use_category,\n",
    "                               ans_filter = None,\n",
    "                               replace_en=True,\n",
    "                               feat_filter_en=True,\n",
    "                               No_tags=False\n",
    "                              )\n",
    "    score_wreplace=[]\n",
    "    for i,score,ans in ans_intargetlist:\n",
    "        score_wreplace.append(quesid2['logit'][i][dset.ans2label[ans]].item())\n",
    "        \n",
    "    dict_use=quesid2\n",
    "    quesid_new={}\n",
    "    quesid_new['image_feats_bert']=[]\n",
    "    quesid_new['image_tags_bert']=[]\n",
    "    quesid_new['image_obj']=[]\n",
    "    for i,(feats_bert,tags_bert,obj_id) in enumerate(zip(dict_use['image_feats_bert'],\n",
    "                                                         dict_use['image_tags_bert'],\n",
    "                                                         dict_use['image_obj'])):\n",
    "        if i in target_select_id:\n",
    "            #print(i,feats_bert.shape)\n",
    "            #print(i,tags_bert.shape)\n",
    "            #print(i,obj_id)\n",
    "            quesid_new['image_feats_bert'].append(feats_bert)\n",
    "            quesid_new['image_tags_bert'].append(tags_bert)\n",
    "            quesid_new['image_obj'].append(obj_id)\n",
    "    #print(len(quesid_new['image_feats_bert']))\n",
    "\n",
    "    hidden_2,_ = plot_cs_singleobj(quesid_new);\n",
    "\n",
    "    replace_obj_id = pseudo_list # not used but need to define\n",
    "\n",
    "    quesid5 = PWI_FixedReplace_QuesModify_Embed(quesfunc,vqa,\n",
    "                               eval_tuple,\n",
    "                               tset,\n",
    "                               tokenizer,\n",
    "                               replace_obj_id,\n",
    "                               use_category,\n",
    "                               ans_filter = None,\n",
    "                               replace_en=True,\n",
    "                               feat_filter_en=True,\n",
    "                               No_tags=False\n",
    "                              )\n",
    "    score_pseudo=[]\n",
    "    for i,score,ans in ans_intargetlist:\n",
    "        score_pseudo.append(quesid5['logit'][i][dset.ans2label[ans]].item())\n",
    "    dict_use=quesid5\n",
    "    quesid_new={}\n",
    "    quesid_new['image_feats_bert']=[]\n",
    "    quesid_new['image_tags_bert']=[]\n",
    "    quesid_new['image_obj']=[]\n",
    "    for i,(feats_bert,tags_bert,obj_id) in enumerate(zip(dict_use['image_feats_bert'],\n",
    "                                                         dict_use['image_tags_bert'],\n",
    "                                                         dict_use['image_obj'])):\n",
    "        if i in target_select_id:\n",
    "            #print(i,feats_bert.shape)\n",
    "            #print(i,tags_bert.shape)\n",
    "            #print(i,obj_id)\n",
    "            quesid_new['image_feats_bert'].append(feats_bert)\n",
    "            quesid_new['image_tags_bert'].append(tags_bert)\n",
    "            quesid_new['image_obj'].append(obj_id)\n",
    "    #print(len(quesid_new['image_feats_bert']))\n",
    "\n",
    "\n",
    "    hidden_3,_ = plot_cs_singleobj(quesid_new);\n",
    "\n",
    "    #if offset_id == 0:\n",
    "    replace_obj_id = hypernymy_list # not used but need to define\n",
    "    quesid4 = PWI_FixedReplace_QuesModify_Embed(quesfunc,vqa,\n",
    "                               eval_tuple,\n",
    "                               tset,\n",
    "                               tokenizer,\n",
    "                               replace_obj_id,\n",
    "                               use_category,\n",
    "                               ans_filter = None,\n",
    "                               replace_en=True,\n",
    "                               feat_filter_en=True,\n",
    "                               No_tags=False\n",
    "                              ) \n",
    "    score_hyponymy=[]\n",
    "    for i,score,ans in ans_intargetlist:\n",
    "        score_hyponymy.append(quesid4['logit'][i][dset.ans2label[ans]].item())\n",
    "    dict_use=quesid4\n",
    "    quesid_new={}\n",
    "    quesid_new['image_feats_bert']=[]\n",
    "    quesid_new['image_tags_bert']=[]\n",
    "    quesid_new['image_obj']=[]\n",
    "    for i,(feats_bert,tags_bert,obj_id) in enumerate(zip(dict_use['image_feats_bert'],\n",
    "                                                         dict_use['image_tags_bert'],\n",
    "                                                         dict_use['image_obj'])):\n",
    "        if i in target_select_id:\n",
    "            #print(i,feats_bert.shape)\n",
    "            #print(i,tags_bert.shape)\n",
    "            #print(i,obj_id)\n",
    "            quesid_new['image_feats_bert'].append(feats_bert)\n",
    "            quesid_new['image_tags_bert'].append(tags_bert)\n",
    "            quesid_new['image_obj'].append(obj_id)\n",
    "    print(len(quesid_new['image_feats_bert']))\n",
    "\n",
    "    hidden_4,_ = plot_cs_singleobj(quesid_new);\n",
    "\n",
    "    cs_index = 0#food_list.index(answer_item)\n",
    "    s_no=[]\n",
    "    s_same=[]\n",
    "    s_diff=[]\n",
    "    s_pseudo=[]\n",
    "    s_hyponymy=[]\n",
    "    #s_notags=[]\n",
    "    for i,score,ans in ans_intargetlist:\n",
    "        if ans==answer_item:\n",
    "            s_no.append(quesid0['logit'][i][dset.ans2label[ans]].item())\n",
    "            s_same.append(quesid1['logit'][i][dset.ans2label[ans]].item())\n",
    "            s_diff.append(quesid2['logit'][i][dset.ans2label[ans]].item())\n",
    "            s_pseudo.append(quesid5['logit'][i][dset.ans2label[ans]].item())\n",
    "            s_hyponymy.append(quesid4['logit'][i][dset.ans2label[ans]].item())\n",
    "            #s_notags.append(quesid6['logit'][i][dset.ans2label[ans]].item())\n",
    "    s_no=np.array(s_no)\n",
    "    s_same=np.array(s_same)\n",
    "    s_diff=np.array(s_diff)\n",
    "    s_pseudo=np.array(s_pseudo)\n",
    "    s_hyponymy=np.array(s_hyponymy)\n",
    "    #s_notags=np.array(s_notags)\n",
    "    p_no=-log2(s_no)\n",
    "    p_sa=-log2(s_same)\n",
    "    p_di=-log2(s_diff)\n",
    "    p_ps=-log2(s_pseudo)\n",
    "    p_hy=-log2(s_hyponymy)\n",
    "    #p_notags=-log2(s_notags)\n",
    "\n",
    "    f_sa=p_no-p_sa\n",
    "    f_di=p_no-p_di\n",
    "    f_ps=p_no-p_ps\n",
    "    f_hy=p_no-p_hy\n",
    "    #f_notags=p_no-p_notags\n",
    "    ans_pos=0\n",
    "    cs_same=[cos_sim(a,b) for a,b in zip(hidden_0[target_object],hidden_1[food_list_cate[ans_pos]])]\n",
    "    cs_diff=[cos_sim(a,b) for a,b in zip(hidden_0[target_object],hidden_2[animal_list[ans_pos]])]\n",
    "    cs_pseudo=[cos_sim(a,b) for a,b in zip(hidden_0[target_object],hidden_3[pseudo_list[ans_pos]])]\n",
    "    cs_hypernymy=[cos_sim(a,b) for a,b in zip(hidden_0[target_object],hidden_4[hypernymy_list[ans_pos]])]\n",
    "    #cs_notags=[cos_sim(a,b) for a,b in zip(hidden_0[answer_item],hidden_4[hypernymy_list[ans_pos]])]\n",
    "\n",
    "    fig, ax = plt.subplots(1, 4,figsize=[14,4],sharey=True)\n",
    "    #fig.suptitle('Horizontally stacked subplots')\n",
    "    ax[0].scatter(f_sa,cs_same,s=1)\n",
    "    ax[0].set_title('same category \\n %s to %s'%(answer_item,food_list_cate[ans_pos]))\n",
    "    ax[0].set_aspect('auto')\n",
    "    ax[0].set_xlim([-10,1])\n",
    "    ax[0].set_ylim([0.1,1.05])\n",
    "    ax[1].scatter(f_di,cs_diff,s=1)\n",
    "    ax[1].set_title('diff category \\n %s to %s'%(answer_item,animal_list[ans_pos]))\n",
    "    ax[1].set_aspect('auto')\n",
    "    ax[1].set_xlim([-10,1])\n",
    "    ax[2].scatter(f_ps,cs_pseudo,s=1)\n",
    "    ax[2].set_title('pseudo \\n %s to %s'%(answer_item,pseudo_list[ans_pos]))\n",
    "    ax[2].set_aspect('auto')\n",
    "    ax[2].set_xlim([-10,1])\n",
    "    ax[3].scatter(f_hy,cs_hypernymy,s=1)\n",
    "    ax[3].set_title('hypernymy \\n %s to %s'%(answer_item,hypernymy_list[ans_pos]))\n",
    "    ax[3].set_aspect('auto')\n",
    "    ax[3].set_xlim([-10,1])\n",
    "#         ax[0].scatter(f_no,cs_notags,s=1)\n",
    "#         ax[4].set_title('hypernymy \\n %s to none'%(answer_item))\n",
    "#         ax[4].set_aspect('auto')\n",
    "#         ax[4].set_xlim([-10,1])\n",
    "    savefig('./save/figures/%s_%s_epoch%d_multilingual_GE.jpg'%(dataset_name,target_object,epoch))\n",
    "    filename0='./save/embed/%s_%s_epoch%d_multilingual_GE.npz'%(dataset_name,target_object,epoch)\n",
    "    savez(filename0,\n",
    "         quesid0=quesid0,\n",
    "         quesid1=quesid1,\n",
    "         quesid2=quesid2,\n",
    "         quesid5=quesid5,\n",
    "         quesid4=quesid4,\n",
    "          quesid6=quesid6,\n",
    "          target_object=target_object,\n",
    "          #offset_id=offset_id,\n",
    "          object_file=object_file,\n",
    "          ans_intargetlist=ans_intargetlist\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b45aef1a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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